{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = 11, 4\n",
    "plt.rcParams['figure.figsize'] = 19, 6\n",
    "plt.rcParams['figure.figsize'] = 22, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALL_SIZE = 22\n",
    "MEDIUM_SIZE = 24\n",
    "BIGGER_SIZE = 32\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import interpolate\n",
    "from scipy.signal import decimate\n",
    "\n",
    "from obspy import UTCDateTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys  #added by an 20200516"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# numpy version =  1.19.5\n",
      "# scipy version =  1.5.2\n",
      "# pandas version =  1.4.2\n",
      "# obspy version =  1.2.2\n"
     ]
    }
   ],
   "source": [
    "import scipy as sp\n",
    "import obspy as ob\n",
    "print(\"# numpy version = \",np.__version__)\n",
    "print(\"# scipy version = \",sp.__version__)\n",
    "print(\"# pandas version = \",pd.__version__)\n",
    "print(\"# obspy version = \",ob.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def heaviside(x):\n",
    "    return 0.5*(np.sign(x) + 1)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def heaviside2(x):\n",
    "    return np.sign(x)    #modified by an 20200420"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a -> eqT \n",
    "# c == 1000000 -> steep step\n",
    "# c  = 1 smooth step \n",
    "def step(x, a, c):\n",
    "    return 1/(1+(np.exp(-2*c *(x-a))) )  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decimal_year(time):\n",
    "        \"\"\"\n",
    "        Return (floating point) decimal year representation of UTCDateTime\n",
    "        input value\n",
    "        \"\"\"\n",
    "        start_of_year = UTCDateTime(time.year, 1, 1).timestamp\n",
    "        end_of_year = UTCDateTime(time.year + 1, 1, 1).timestamp\n",
    "        timestamp = time.timestamp\n",
    "        year_fraction = ((timestamp - start_of_year) /\n",
    "                         (end_of_year - start_of_year))\n",
    "        return time.year + year_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_decimal(start):\n",
    "    year = int(start)\n",
    "    rem = start - year\n",
    "    base = datetime(year, 1, 1)\n",
    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
    "    #print(result)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## inc long term trend\n",
    "def func(x, e1, e2, f, A, B, e3, e4):\n",
    "  return   A + B*x+e1*np.sin(2*math.pi*f*x) + e2*np.cos(2*math.pi*f*x) + \\\n",
    "                        e3*np.sin(4*math.pi*f*x) + e4*np.cos(4*math.pi*f*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## inc long term trend\n",
    "def func_eq(x, e1, e2, f, A, B, e3, e4, C, D, E, eqT):\n",
    "  return   A + B*x+e1*np.sin(2*math.pi*f*x) + e2*np.cos(2*math.pi*f*x) + \\\n",
    "                        e3*np.sin(4*math.pi*f*x) + e4*np.cos(4*math.pi*f*x) +  \\\n",
    "                        (C+  (D*np.exp(-(x-eqT)/E) ) )*heaviside(x-eqT) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_eq_step(x, e1, e2, f, A, B, e3, e4, C, D, E, eqT, sc):\n",
    "  return   A + B*x+e1*np.sin(2*math.pi*f*x) + e2*np.cos(2*math.pi*f*x) + \\\n",
    "                        e3*np.sin(4*math.pi*f*x) + e4*np.cos(4*math.pi*f*x) +  \\\n",
    "                        (C+  (D*np.exp(-(x-eqT)/E) ) )*step(x,eqT, sc) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_eq2(x, e1, e2, f, A, B, e3, e4, C, D, E, eqT, h1, h2):\n",
    "  return   A + B*x+e1*np.sin(2*math.pi*f*x) + e2*np.cos(2*math.pi*f*x) + \\\n",
    "                        e3*np.sin(4*math.pi*f*x) + e4*np.cos(4*math.pi*f*x) +  \\\n",
    "                        (C+  (D*np.exp(-(x-eqT)/E) ) )* h1*(heaviside2(x-eqT)+ h2) #modified by an 20200420"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2OPT = 0 # if 1 use heaviside2 func__2\n",
    "#h2OPT = 1 # if 1 use heaviside2 func__2\n",
    "stepOPT  = 1 # use step func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dvv_2sdOPT = 1\n",
    "dvv_2sd_max = 0.2 # %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasonality_off = 1 # no seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "longterm_trend_off = 1 # no long_term_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_on = 1 # no long_term_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#smooth_tw = \"5D\" # 5days\n",
    "#smooth_tw = \"30D\" # 30 days  #modified by an 20200407\n",
    "#smooth_tw = \"10D\" # 1 days  #modified by an 20200407\n",
    "#add\n",
    "\n",
    "# use 1D\n",
    "smooth_tw = \"1D\" # 1 days  #modified by an 20200407\n",
    "#smooth_tw = \"5D\" # 1 days  #modified by an 20200407\n",
    "\n",
    "\n",
    "### \n",
    "#msnoise stack xxx dayx \n",
    "#smoothing here yyy days\n",
    "#xxx < yyy\n",
    "#xxxx= 5 days , \n",
    "#yyy=10\n",
    "\n",
    "#xxx=30 days\n",
    "#yyy= 1 days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sta = \"CI_CLC_CI_SRT\" #modified by an 20200409 l\n",
    "#sta =  \"CI_WRC2_PB_B916\"\n",
    "\n",
    "\n",
    "#sta =  \"CI_CCC_CI_SRT\"\n",
    "#\n",
    "sta =  \"CI_CCC_PB_B916\"\n",
    "\n",
    "#sta =  \"CI_CLC_CI_LRL\"\n",
    "#sta =  \"CI_CWC_CI_LRL\"\n",
    "#\n",
    "# error\n",
    "#sta = \"CI_JRC2_CI_CWC\"\n",
    "# error\n",
    "#sta = \"CI_CCC_CI_CWC\"\n",
    "sta = \"CI_CCC_CI_TOW2\"\n",
    "\n",
    "#sta = \"CI_CCC_CI_CGO\"\n",
    "#sta = \"CI_CCC_CI_CWC\"\n",
    "\n",
    "#sta = \"CI_DAW_CI_SRT\"\n",
    "\n",
    "#sta = \"CI_WMF_CI_LRL\"  # no return\n",
    "#sta = \"CI_JRC2_CI_WMF\" # Coso good one\n",
    "#sta = \"CI_WCS2_CI_WMF\" # Coso good one revocery question\n",
    "sta =  \"CI_DAW_CI_SRT\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xc_para = \"ZRT.01.030.dttwithoffset.dynamic_2.0_20.0_30.0_both_0.85_0.1_0.5.out\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/taira/work/python_work/curve_fit_RC/dttwithoffset_out/CI_DAW_CI_SRT.ZRT.01.030.dttwithoffset.dynamic_2.0_20.0_30.0_both_0.85_0.1_0.5.out\n"
     ]
    }
   ],
   "source": [
    "#dvv_fi = \"./\"+sta+\".ZRT.01.030.dttwithoffset.dynamic_2.0_20.0_30.0_both_0.85_0.1_0.5.out\"  #modified by an 20200409 lagtop\n",
    "\n",
    "data_dir = \"/Users/taira/work/python_work/curve_fit_RC/dttwithoffset_out\"\n",
    "\n",
    "dvv_fi = \"./\"+sta+\".\"+xc_para\n",
    "dvv_fi = data_dir+\"/\"+sta+\".\"+xc_para\n",
    "print(dvv_fi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dvv_data = pd.read_csv(dvv_fi,   \n",
    "                    sep=\" \",names=[\"timeUTC\", \"dtt\", \"dtt_sd\", \"number\"],header=None)  #modified by an 20200409\n",
    "dvv_data['dvv'] = dvv_data['dtt'] * (-100)  #modified by an 20200409\n",
    "dvv_data['dvv_2sd'] = dvv_data['dtt_sd'] * (200)  \n",
    "\n",
    "dvv_data['time'] = pd.to_datetime(dvv_data['timeUTC'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "if dvv_2sdOPT == 1:\n",
    "    data_select = dvv_data[   dvv_data['dvv_2sd'] <= dvv_2sd_max   ]\n",
    "\n",
    "else:\n",
    "    data_select = dvv_data.copy()\n",
    "\n",
    "# old just copy\n",
    "#\n",
    "#catalog_data_eq_normalF = catalog_data_eq[catalog_data_eq['fault_type']  ==  0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_select.set_index('time', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dvv_median = data_select.resample(smooth_tw, label='right').median()\n",
    "dvv_median = dvv_median.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(dvv_data['dvv_2sd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/taira/work/python_work/curve_fit_RC/dttwithoffset_out/CI_DAW_CI_SRT.ZRT.01.030.dttwithoffset.dynamic_2.0_20.0_30.0_both_0.85_0.1_0.5.out\n",
      "2019-01-02 23:59:59\n"
     ]
    }
   ],
   "source": [
    "print(dvv_fi)\n",
    "print(dvv_data.time[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "decimal_time_dvv = np.zeros(len(dvv_median.index))\n",
    "decimal_time_lapse_dvv = np.zeros(len(dvv_median.index))\n",
    "decimal_ref_time_dvv = 2019.00000  #modified by an 20200406\n",
    "for i in range(len(dvv_median.index)):\n",
    "    #print(i)\n",
    "    dt = UTCDateTime(dvv_median.index[i])\n",
    "    decimal_time_dvv[i] = decimal_year(dt)\n",
    "\n",
    "\n",
    "decimal_time_lapse_dvv = decimal_time_dvv - decimal_ref_time_dvv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(decimal_time_lapse_dvv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-07-06T03:19:53.000000Z\n",
      "0.5099693366312295\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#decimal_ref_time = 2002.00000  #modified by an 20200406\n",
    "decimal_ref_time = 2019.00000  #modified by an 20200406\n",
    "\n",
    "\n",
    "RC71eq = UTCDateTime(\"2019-07-06 03:19:53\")   #modified by an 20200407 correct one\n",
    "#RC71eq= UTCDateTime(\"2019-07-19 03:19:53\")   #modified by an 20200420 add to 30days\n",
    "    \n",
    "    \n",
    "print(RC71eq )\n",
    "decimal_time_RC71eq  = decimal_year(RC71eq)\n",
    "decimal_ref_time = 2019.00000    #modified by an 20200406\n",
    "decimal_time_lapse_RC71eq  = decimal_time_RC71eq  - decimal_ref_time_dvv\n",
    "print (decimal_time_lapse_RC71eq ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(decimal_time_lapse_dvv, dvv_median.dvv*1)\n",
    "plt.plot(decimal_time_lapse_dvv, dvv_median.dvv*1, 'o' , label='observed data after medinan filter with '+smooth_tw)\n",
    "\n",
    "\n",
    "plt.fill_between(decimal_time_lapse_dvv, (dvv_median.dvv-dvv_median.dvv_2sd), \n",
    "                 (dvv_median.dvv+dvv_median.dvv_2sd), alpha=0.25,linewidth = 2.5, color=\"orange\")\n",
    "\n",
    "\n",
    "\n",
    "#plt.fill_between(ch02_data_amp['time'], (ch02_data_amp['amp_med']-ch02_data_amp['amp_l1']*coeff), \n",
    "#                 (ch02_data_amp['amp_med']+ch02_data_amp['amp_l1']*coeff), alpha=0.25,linewidth = 2.5, color=\"orange\")\n",
    "\n",
    "\n",
    "plt.xlabel('Year from 2019')  #modified by an 20200406\n",
    "#    plt.ylabel('Viso (m/s)')\n",
    "#    plt.title('Viso Station: '+sta)\n",
    "\n",
    "#plt.ylim((-0.2,0.2)) \n",
    "\n",
    "plt.ylabel('Dvv') #modified by an 20200407\n",
    "plt.title('Dvv Station: '+sta) #modified by an 20200409\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00273972602736\n",
      "1.00273224044\n"
     ]
    }
   ],
   "source": [
    "print(decimal_time_lapse_dvv[0])\n",
    "print(decimal_time_lapse_dvv[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0127397260274\n",
      "0.992732240437\n"
     ]
    }
   ],
   "source": [
    "st_time = decimal_time_lapse_dvv[0] + 0.01\n",
    "et_time =  decimal_time_lapse_dvv[-1] - 0.01\n",
    "print(st_time)\n",
    "print(et_time)\n",
    "#st_time  = 0.65\n",
    "#et_time =  5.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "### adds\n",
    "dttime = 0.01 \n",
    "### adds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = interpolate.interp1d(decimal_time_lapse_dvv, dvv_median.dvv*1, kind=\"cubic\")\n",
    "xnew_dvv = np.arange(st_time, et_time, dttime)\n",
    "ynew_dvv = f(xnew_dvv)   # use interpolation function returned by `interp1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(xnew_dvv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ynew_dvv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9c20a12040>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(decimal_time_lapse_dvv, dvv_median.dvv*1, 'o' , label='observed data after medinan filter with '+smooth_tw)\n",
    "plt.plot(xnew_dvv, ynew_dvv, '-' , label='interpolated data with cubic spline')\n",
    "   \n",
    "#plt.ylim((-0.7,0.1)) #modified by an 20200407\n",
    "#plt.xlabel('Year from 2002') #modified by an 20200407\n",
    "plt.xlabel('Year from 2019') #modified by an 20200407\n",
    "plt.ylabel('dvv (%)')\n",
    "plt.title('dvv Station: '+sta)\n",
    "plt.grid(True)\n",
    "#plt.legend(loc='upper left') #modified by an 20200407\n",
    "plt.legend(loc='upper right') #modified by an 20200407"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "xnew_fit = xnew_dvv\n",
    "ynew_fit = ynew_dvv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_decimal(start):\n",
    "    year = int(start)\n",
    "    rem = start - year\n",
    "    base = datetime(year, 1, 1)\n",
    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
    "    #print(result)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#xnew_fitOUT = xnew_fit + 2008\n",
    "#xnew_fitOUT = xnew_fit + 2002 #modified by an 20200409\n",
    "xnew_fitOUT = xnew_fit + 2019 #modified by an 20200409"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019.01273973\n"
     ]
    }
   ],
   "source": [
    "print(xnew_fitOUT[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#year_time = np.zeros(len(decimal_time))\n",
    "#year_time = (len(decimal_time))\n",
    "year_time = []\n",
    "#decimal_time_lapse = np.zeros(len(time))\n",
    "\n",
    "for i in range(len(xnew_fitOUT)):\n",
    "    #print(i)\n",
    "    yt_tmp = year_decimal(xnew_fitOUT[i])\n",
    "    yt_tmp2 = yt_tmp.strftime(\"%Y-%m-%dT%H:%M:%S.%f\")\n",
    "\n",
    "    #decimal_time[i] = decimal_year(dt)\n",
    "    #year_time.append(yt_tmp)\n",
    "    year_time.append(yt_tmp2)\n",
    "    \n",
    "#decimal_time_lapse = decimal_time - decimal_ref_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-05T15:35:59.999998\n"
     ]
    }
   ],
   "source": [
    "print(year_time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_timedf = pd.DataFrame(year_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5099693366312295\n"
     ]
    }
   ],
   "source": [
    "print(decimal_time_lapse_RC71eq)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seasonality \n",
    "e1_min = -np.inf\n",
    "e1_max = np.inf\n",
    "e2_min = -np.inf\n",
    "e2_max = np.inf\n",
    "# seasonality \n",
    "e3_min = -np.inf\n",
    "e3_max = np.inf\n",
    "e4_min = -np.inf\n",
    "e4_max = np.inf\n",
    "\n",
    "if seasonality_off:\n",
    "    e1_min = -0.000001\n",
    "    e1_max = 0.000001\n",
    "    e2_min = -0.000001\n",
    "    e2_max = 0.000001\n",
    "    # seasonality \n",
    "    e3_min = -0.000001\n",
    "    e3_max = 0.000001\n",
    "    e4_min = -0.000001\n",
    "    e4_max = 0.000001\n",
    "\n",
    "#  freq. seasonality \n",
    "f_min = 0.999999\n",
    "f_max = 1.000001\n",
    "\n",
    "# offset \n",
    "A_min = -np.inf\n",
    "A_max = np.inf\n",
    "\n",
    "# liner trend\n",
    "B_min = -np.inf\n",
    "B_max = np.inf\n",
    "if longterm_trend_off:\n",
    "    B_min = -0.00001\n",
    "    B_max = 0.000001\n",
    "    \n",
    "# no longer used should be 0\n",
    "C_min = -0.0000001\n",
    "C_max = 0.0000001\n",
    "if C_on:\n",
    "    C_min = -5\n",
    "    C_max = 5\n",
    "\n",
    "# coseismic change. currently assuming negative value\n",
    "D_min = -np.inf\n",
    "D_max = 0\n",
    "#D_max = np.inf\n",
    "\n",
    "# recover time -> should be positive\n",
    "E_min = 0\n",
    "E_max = np.inf\n",
    "#E_max = 10.0\n",
    "\n",
    "\n",
    "# eqT\n",
    "#eqT_low = decimal_time_lapse_RC71eq   -0.00001\n",
    "#eqT_high = decimal_time_lapse_RC71eq +0.00001\n",
    "\n",
    "eqT_low = decimal_time_lapse_RC71eq   -0.000001\n",
    "eqT_high = decimal_time_lapse_RC71eq +1\n",
    "eqT_high = decimal_time_lapse_RC71eq +0.1\n",
    "#eqT_high = decimal_time_lapse_RC71eq +0.05\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   #  e1     e2                       f           A          B             e3      e4        C           D           E         eqT             C2                  \n",
    "#    ([-np.inf, -np.inf,   0.999999,  -np.inf,  -np.inf, -np.inf, -np.inf, -np.inf, -np.inf,      0,    eqT_low,   -np.inf,  -np.inf,           0,   eqT_low2],\n",
    "#[     np.inf,  np.inf,   1.000001,    np.inf,   np.inf,  np.inf,   np.inf,  np.inf,         0, np.inf,  eqT_high,    np.inf,          0,    np.inf,    eqT_high2]) \n",
    "\n",
    "param_bounds_eq=([e1_min, e2_min, f_min,  A_min, B_min, e3_min, e4_min, C_min, D_min, E_min,   eqT_low],\n",
    "                                     [e1_max, e2_max, f_max,  A_max, B_max, e3_max, e4_max, C_max, D_max, E_max,   eqT_high])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "e1_init = 1.0\n",
    "e2_init = 1.0\n",
    "e3_init = 1.0\n",
    "e4_init = 1.0\n",
    "\n",
    "if seasonality_off:\n",
    "    e1_init = 0.0\n",
    "    e2_init = 0.0\n",
    "    e3_init = 0.0\n",
    "    e4_init = 0.0\n",
    "\n",
    "f_init = 1.0\n",
    "A_init = 0.1\n",
    "\n",
    "B_init = 0.1\n",
    "if longterm_trend_off:\n",
    "    B_init = 0.0\n",
    "\n",
    "C_init = 0.0\n",
    "D_init = -1.0\n",
    "E_init = 1.0\n",
    "\n",
    "\n",
    "popt, pcov = curve_fit(func_eq, xnew_fit, ynew_fit, p0=(e1_init, e2_init,f_init, A_init, B_init, e3_init, e4_init, C_init, D_init, E_init, decimal_time_lapse_RC71eq), bounds=param_bounds_eq)     #modified by an 20200421\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if h1 = 0.5 && h2 = 1.0 -> \n",
    "\n",
    "if h2OPT:\n",
    "    h1_init = 0.5\n",
    "    h2_init = 1.0\n",
    "\n",
    "    h1_min =  0.499999\n",
    "    h1_max = 0.500001\n",
    "\n",
    "    h2_min =  0.9999999\n",
    "    h2_max = 1.00001\n",
    "    \n",
    "    #h1_min =  -np.inf\n",
    "    #h1_min = 0.0\n",
    "    #h1_max = np.inf\n",
    "\n",
    "    h2_min =  -np.inf\n",
    "    h2_max = np.inf\n",
    "    h2_min =  0.99\n",
    "    h2_max = 1.01\n",
    "        \n",
    "    param_bounds_eq=([e1_min, e2_min, f_min,  A_min, B_min, e3_min, e4_min, C_min, D_min, E_min,   eqT_low, h1_min, h2_min],\n",
    "                                     [e1_max, e2_max, f_max,  A_max, B_max, e3_max, e4_max, C_max, D_max, E_max,   eqT_high, h2_max, h2_max])\n",
    "\n",
    "    popt, pcov = curve_fit(func_eq2, xnew_fit, ynew_fit, p0=(e1_init, e2_init,f_init, A_init, B_init, e3_init, e4_init, C_init, D_init, E_init, decimal_time_lapse_RC71eq, h1_init, h2_init), bounds=param_bounds_eq)     #modified by an 20200421\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "if stepOPT:\n",
    "\n",
    "    sc_init = 5.0\n",
    "    sc_min =  5.0\n",
    "    sc_max =  100\n",
    "\n",
    "    #sc_init = 1.0\n",
    "    #sc_min =  1.0\n",
    "    #sc_max =  100 \n",
    "    sc_init = 10.0\n",
    "    sc_min =  10.0\n",
    "    sc_max =  1000     \n",
    "    param_bounds_eq=([e1_min, e2_min, f_min,  A_min, B_min, e3_min, e4_min, C_min, D_min, E_min,   eqT_low, sc_min],\n",
    "                                     [e1_max, e2_max, f_max,  A_max, B_max, e3_max, e4_max, C_max, D_max, E_max,   eqT_high, sc_max])\n",
    "\n",
    "    popt, pcov = curve_fit(func_eq_step, xnew_fit, ynew_fit, p0=(e1_init, e2_init,f_init, A_init, B_init, e3_init, e4_init, C_init, D_init, E_init, decimal_time_lapse_RC71eq, sc_init), bounds=param_bounds_eq)     #modified by an 20200421\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -1.00000000e-06   1.00000000e-06   1.00000100e+00   8.48540515e-04\n",
      "  -1.00000000e-05   1.00000000e-06   1.00000000e-06  -1.63007548e-01\n",
      "  -2.55685720e-01   8.57599265e-02   5.66048045e-01   3.22660991e+01]\n",
      "[[  3.43577317e-03  -9.44558392e-03   1.12779776e+02   8.36828328e-03\n",
      "   -4.27631800e-02   1.72709438e-03   7.71811823e-04   4.41425130e-02\n",
      "   -2.79638520e-02   1.66124824e-02  -3.68125238e-04   4.63120482e-01]\n",
      " [ -9.44558392e-03   2.86535077e-02  -2.98080337e+02  -2.57185224e-02\n",
      "    1.28475534e-01  -5.28094808e-03  -2.23149870e-03  -1.32875931e-01\n",
      "    8.32543040e-02  -5.25750932e-02   1.43141351e-03  -1.30704815e+00]\n",
      " [  1.12779776e+02  -2.98080337e+02   1.00418098e+07   2.78656945e+02\n",
      "   -1.43508701e+03   7.21525458e+01  -5.38619624e+00   1.39753502e+03\n",
      "   -9.32655643e+02   4.55159558e+02  -3.75098780e+00   1.40668551e+04]\n",
      " [  8.36828328e-03  -2.57185224e-02   2.78656945e+02   2.32925305e-02\n",
      "   -1.15840654e-01   4.72593269e-03   1.91884637e-03   1.19404329e-01\n",
      "   -7.44569979e-02   4.73767369e-02  -1.31605380e-03   1.19325424e+00]\n",
      " [ -4.27631800e-02   1.28475534e-01  -1.43508701e+03  -1.15840654e-01\n",
      "    5.79451623e-01  -2.36293625e-02  -9.68257686e-03  -5.97320401e-01\n",
      "    3.73558703e-01  -2.34495306e-01   6.26149802e-03  -6.04449271e+00]\n",
      " [  1.72709438e-03  -5.28094808e-03   7.21525458e+01   4.72593269e-03\n",
      "   -2.36293625e-02   1.08769525e-03   3.19395969e-04   2.43522376e-02\n",
      "   -1.56280335e-02   9.52735870e-03  -2.43371075e-04   2.13952699e-01]\n",
      " [  7.71811823e-04  -2.23149870e-03  -5.38619624e+00   1.91884637e-03\n",
      "   -9.68257686e-03   3.19395969e-04   3.53256247e-04   1.03627282e-02\n",
      "   -6.47593096e-03   4.19419614e-03  -1.15974170e-04   1.00427659e-01]\n",
      " [  4.41425130e-02  -1.32875931e-01   1.39753502e+03   1.19404329e-01\n",
      "   -5.97320401e-01   2.43522376e-02   1.03627282e-02   6.17390543e-01\n",
      "   -3.85752719e-01   2.43664251e-01  -6.61352230e-03   6.21266296e+00]\n",
      " [ -2.79638520e-02   8.32543040e-02  -9.32655643e+02  -7.44569979e-02\n",
      "    3.73558703e-01  -1.56280335e-02  -6.47593096e-03  -3.85752719e-01\n",
      "    2.46328441e-01  -1.49582894e-01   3.60769259e-03  -3.57744930e+00]\n",
      " [  1.66124824e-02  -5.25750932e-02   4.55159558e+02   4.73767369e-02\n",
      "   -2.34495306e-01   9.52735870e-03   4.19419614e-03   2.43664251e-01\n",
      "   -1.49582894e-01   9.97798500e-02  -3.11554348e-03   2.46232002e+00]\n",
      " [ -3.68125238e-04   1.43141351e-03  -3.75098780e+00  -1.31605380e-03\n",
      "    6.26149802e-03  -2.43371075e-04  -1.15974170e-04  -6.61352230e-03\n",
      "    3.60769259e-03  -3.11554348e-03   1.58535862e-04  -8.51909182e-02]\n",
      " [  4.63120482e-01  -1.30704815e+00   1.40668551e+04   1.19325424e+00\n",
      "   -6.04449271e+00   2.13952699e-01   1.00427659e-01   6.21266296e+00\n",
      "   -3.57744930e+00   2.46232002e+00  -8.51909182e-02   1.03424665e+02]]\n",
      "e1 =  -0.00000 +/- 0.05862\n",
      "e2 = 0.00000 +/- 0.16927\n",
      "f =  1.00000 +/- 3168.88147\n",
      "A =  0.00085 +/- 0.15262\n",
      "B =  -0.00001 +/- 0.76122\n",
      "e3 =  0.00000 +/- 0.03298\n",
      "e4 = 0.00000 +/- 0.01880\n"
     ]
    }
   ],
   "source": [
    "print (popt)\n",
    "print (pcov)\n",
    "#Print results\n",
    "print(\"e1 =  %.5f +/- %.5f\" % (popt[0], math.sqrt(pcov[0, 0])))\n",
    "print(\"e2 = %.5f +/- %.5f\" % (popt[1], math.sqrt(pcov[1, 1])))\n",
    "print(\"f =  %.5f +/- %.5f\" % (popt[2], math.sqrt(pcov[2, 2])))\n",
    "print(\"A =  %.5f +/- %.5f\" % (popt[3], math.sqrt(pcov[3, 3])))\n",
    "print(\"B =  %.5f +/- %.5f\" % (popt[4], math.sqrt(pcov[4, 4])))\n",
    "print(\"e3 =  %.5f +/- %.5f\" % (popt[5], math.sqrt(pcov[5, 5])))\n",
    "print(\"e4 = %.5f +/- %.5f\" % (popt[6], math.sqrt(pcov[6, 6])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C =  -0.16301 +/- 0.78574\n",
      "D =  -0.25569 +/- 0.49631\n",
      "E =  0.08576 +/- 0.31588\n",
      "eqT = 0.56605 +/- 0.01259\n"
     ]
    }
   ],
   "source": [
    "print(\"C =  %.5f +/- %.5f\" % (popt[7], math.sqrt(pcov[7, 7])))\n",
    "print(\"D =  %.5f +/- %.5f\" % (popt[8], math.sqrt(pcov[8, 8])))\n",
    "print(\"E =  %.5f +/- %.5f\" % (popt[9], math.sqrt(pcov[9, 9])))\n",
    "print(\"eqT = %.5f +/- %.5f\" % (popt[10], math.sqrt(pcov[10, 10])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "if h2OPT:\n",
    "\n",
    "    print(\"h1 = %.5f +/- %.5f\" % (popt[11], math.sqrt(pcov[11, 11]))) #modified by an 20200421\n",
    "    print(\"h2 = %.5f +/- %.5f\" % (popt[12], math.sqrt(pcov[12, 12]))) #modified by an 20200421"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sc = 32.26610 +/- 10.16979\n"
     ]
    }
   ],
   "source": [
    "if stepOPT:\n",
    "    print(\"sc = %.5f +/- %.5f\" % (popt[11], math.sqrt(pcov[11, 11]))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e1 =  -0.00000 \n",
      "e2 =  0.00000 \n",
      "e =  0.00000 \n",
      "f(1/year) =  1.00000 \n",
      "A =  0.00085 \n",
      "B =  -0.00001 \n",
      "e1sd =  0.05862 \n",
      "e2sd =  0.16927 \n",
      "f(1/year)sd =  3168.88147 \n",
      "Asd =  0.15262 \n",
      "Bsd =  0.76122 \n",
      "phi =  -0.78540 \n",
      "phi_deg =  -45.00000 \n",
      "phi_deg2 =  315.00000 \n",
      "e3 =  0.00000 \n",
      "e4 =  0.00000 \n",
      "e3sd =  0.03298 \n",
      "e4sd =  0.01880 \n",
      "e4p =  0.00000 \n",
      "phi4p =  0.78540 \n",
      "phi4p_deg =  45.00000 \n",
      "phi4p_deg2 =  405.00000 \n",
      "phi_day =  -45.75000 \n",
      "phi4p_day =  45.75000 \n",
      "e_ratio =  1.00000 \n"
     ]
    }
   ],
   "source": [
    "perr = np.sqrt(np.diag(pcov))\n",
    "\n",
    "e1=popt[0]\n",
    "e2=popt[1]\n",
    "f=popt[2]\n",
    "A=popt[3]\n",
    "B=popt[4]\n",
    "\n",
    "e3=popt[5]\n",
    "e4=popt[6]\n",
    "\n",
    "e1sd = math.sqrt(pcov[0, 0])\n",
    "e2sd = math.sqrt(pcov[1, 1])\n",
    "fsd = math.sqrt(pcov[2, 2])\n",
    "\n",
    "Asd = math.sqrt(pcov[3, 3])\n",
    "Bsd = math.sqrt(pcov[4, 4])\n",
    "e3sd = math.sqrt(pcov[5, 5])\n",
    "e4sd = math.sqrt(pcov[6, 6])\n",
    "\n",
    "\n",
    "#e1 = 0.5\n",
    "#e2 = 0.5\n",
    "phi=math.atan2(1*e1,e2)\n",
    "phi_deg=(phi*180.0)/math.pi\n",
    "phi_deg2=360+phi_deg\n",
    "\n",
    "phi_day = (phi_deg / 360.0) *366.0\n",
    "\n",
    "e1_2=e1*e1\n",
    "e2_2=e2*e2\n",
    "etmp=e1_2+e2_2\n",
    "e=math.sqrt(etmp)\n",
    "\n",
    "\n",
    "phi4p=math.atan2(1*e3,e4)\n",
    "phi4p_deg=(phi4p*180.0)/math.pi\n",
    "phi4p_deg2=360+phi4p_deg\n",
    "\n",
    "phi4p_day = (phi4p_deg / 360.0) *366.0\n",
    "\n",
    "e3_2=e3*e3\n",
    "e4_2=e4*e4\n",
    "etmp4p=e3_2+e4_2\n",
    "e4p=math.sqrt(etmp4p)\n",
    "\n",
    "\n",
    "e_ratio =  e4p / e\n",
    "\n",
    "\n",
    "print(\"e1 =  %.5f \"% (e1))\n",
    "\n",
    "print(\"e2 =  %.5f \"% (e2))\n",
    "print(\"e =  %.5f \"% (e))\n",
    "print(\"f(1/year) =  %.5f \"% (f))\n",
    "print(\"A =  %.5f \"% (A))\n",
    "print(\"B =  %.5f \"% (B))\n",
    "\n",
    "\n",
    "\n",
    "print(\"e1sd =  %.5f \"% (e1sd))\n",
    "print(\"e2sd =  %.5f \"% (e2sd))\n",
    "print(\"f(1/year)sd =  %.5f \"% (fsd))\n",
    "print(\"Asd =  %.5f \"% (Asd))\n",
    "print(\"Bsd =  %.5f \"% (Bsd))\n",
    "\n",
    "\n",
    "print(\"phi =  %.5f \"% (phi))\n",
    "print(\"phi_deg =  %.5f \"% (phi_deg))\n",
    "print(\"phi_deg2 =  %.5f \"% (phi_deg2))\n",
    "\n",
    "print(\"e3 =  %.5f \"% (e3))\n",
    "print(\"e4 =  %.5f \"% (e4))\n",
    "\n",
    "\n",
    "print(\"e3sd =  %.5f \"% (e3sd))\n",
    "print(\"e4sd =  %.5f \"% (e4sd))\n",
    "\n",
    "print(\"e4p =  %.5f \"% (e4p))\n",
    "\n",
    "\n",
    "\n",
    "print(\"phi4p =  %.5f \"% (phi4p))\n",
    "print(\"phi4p_deg =  %.5f \"% (phi4p_deg))\n",
    "print(\"phi4p_deg2 =  %.5f \"% (phi4p_deg2))\n",
    "\n",
    "\n",
    "print(\"phi_day =  %.5f \"% (phi_day))\n",
    "print(\"phi4p_day =  %.5f \"% (phi4p_day))\n",
    "print(\"e_ratio =  %.5f \"% (e_ratio))\n",
    "\n",
    "# cos (w - phi) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C =  -0.16301 \n",
      "D =  -0.25569 \n",
      "E =  0.08576 \n",
      "eqT =  0.56605 \n",
      "Csd =  0.78574 \n",
      "Dsd =  0.49631 \n",
      "Esd =  0.31588 \n",
      "eqTsd =  0.01259 \n"
     ]
    }
   ],
   "source": [
    "C=popt[7]\n",
    "D=popt[8]\n",
    "E=popt[9]\n",
    "eqT=popt[10]\n",
    "\n",
    "Csd = math.sqrt(pcov[7, 7])\n",
    "Dsd = math.sqrt(pcov[8, 8])\n",
    "Esd = math.sqrt(pcov[9, 9])\n",
    "eqTsd = math.sqrt(pcov[10, 10])\n",
    "\n",
    "\n",
    "print(\"C =  %.5f \"% (C))\n",
    "print(\"D =  %.5f \"% (D))\n",
    "print(\"E =  %.5f \"% (E))\n",
    "print(\"eqT =  %.5f \"% (eqT))\n",
    "\n",
    "print(\"Csd =  %.5f \"% (Csd))\n",
    "print(\"Dsd =  %.5f \"% (Dsd))\n",
    "print(\"Esd =  %.5f \"% (Esd))\n",
    "print(\"eqTsd =  %.5f \"% (eqTsd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "if h2OPT:\n",
    "\n",
    "    h1=popt[11] #modified by an 20200421\n",
    "    h2=popt[12] #modified by an 20200421\n",
    "\n",
    "    print(\"h1 =  %.5f \"% (h1)) #modified by an 20200421\n",
    "    print(\"h2 =  %.5f \"% (h2)) #modified by an 20200421\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sc =  32.26610 \n",
      "scsd =  10.16979 \n"
     ]
    }
   ],
   "source": [
    "if stepOPT:\n",
    "    sc=popt[11] \n",
    "    scsd = math.sqrt(pcov[11, 11])\n",
    "\n",
    "    print(\"sc =  %.5f \"% (sc)) \n",
    "    print(\"scsd =  %.5f \"% (scsd)) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "resi_fit = np.zeros(len(xnew_fit))\n",
    "yOUT_fit = np.zeros(len(ynew_fit))\n",
    "yOUT_fit_1st = np.zeros(len(ynew_fit))\n",
    "yOUT_fit_2nd = np.zeros(len(ynew_fit))\n",
    "for i in range(len(xnew_fit)):\n",
    "  # print xdata[i]\n",
    "    #yOUT_fit[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) + e2*np.cos(2*math.pi*f*xnew_fit[i])\n",
    "        yOUT_fit[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "                                         e2*np.cos(2*math.pi*f*xnew_fit[i])+\\\n",
    "                                     e3*np.sin(4*math.pi*f*xnew_fit[i]) + \\\n",
    "                                     e4*np.cos(4*math.pi*f*xnew_fit[i])+ \\\n",
    "                                     (C+ (D*np.exp(-(xnew_fit[i]-eqT)/E) )  ) *heaviside(xnew_fit[i] -eqT) #modified by an 20200421\n",
    "\n",
    "        \n",
    "#    yOUT_fit[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "#                                    e2*np.cos(2*math.pi*f*xnew_fit[i])+\\\n",
    "#                                    e3*np.sin(4*math.pi*f*xnew_fit[i]) + \\\n",
    "#                                    e4*np.cos(4*math.pi*f*xnew_fit[i])+ \\\n",
    "#                                    (C+ (D*np.exp(-(xnew_fit[i]-eqT)/E) )  ) *h1*(heaviside(xnew_fit[i] -eqT)+ h2) #modified by an 20200421\n",
    "  #print yOUT[i]\n",
    "        resi_fit[i]=ynew_fit[i]-yOUT_fit[i]\n",
    "  #print resi[i]\n",
    "        yOUT_fit_1st[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "                                    e2*np.cos(2*math.pi*f*xnew_fit[i])\n",
    "        yOUT_fit_2nd[i]= A + B*xnew_fit[i]+e3*np.sin(4*math.pi*f*xnew_fit[i]) +\\\n",
    "                                    e4*np.cos(4*math.pi*f*xnew_fit[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "if h2OPT:\n",
    "    resi_fit = np.zeros(len(xnew_fit))\n",
    "    yOUT_fit = np.zeros(len(ynew_fit))\n",
    "    yOUT_fit_1st = np.zeros(len(ynew_fit))\n",
    "    yOUT_fit_2nd = np.zeros(len(ynew_fit))\n",
    "    for i in range(len(xnew_fit)):\n",
    "        yOUT_fit[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "                                       e2*np.cos(2*math.pi*f*xnew_fit[i])+\\\n",
    "                                    e3*np.sin(4*math.pi*f*xnew_fit[i]) + \\\n",
    "                                    e4*np.cos(4*math.pi*f*xnew_fit[i])+ \\\n",
    "                                    (C+ (D*np.exp(-(xnew_fit[i]-eqT)/E) )  ) *h1*(heaviside2(xnew_fit[i] -eqT)+ h2) #modified by an 20200421\n",
    "  #print yOUT[i]\n",
    "        resi_fit[i]=ynew_fit[i]-yOUT_fit[i]\n",
    "  #print resi[i]\n",
    "        yOUT_fit_1st[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "                                    e2*np.cos(2*math.pi*f*xnew_fit[i])\n",
    "        yOUT_fit_2nd[i]= A + B*xnew_fit[i]+e3*np.sin(4*math.pi*f*xnew_fit[i]) +\\\n",
    "                                    e4*np.cos(4*math.pi*f*xnew_fit[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def func_eq_step(x, e1, e2, f, A, B, e3, e4, C, D, E, eqT, sc):\n",
    "\n",
    "if stepOPT:\n",
    "    resi_fit = np.zeros(len(xnew_fit))\n",
    "    yOUT_fit = np.zeros(len(ynew_fit))\n",
    "    yOUT_fit_1st = np.zeros(len(ynew_fit))\n",
    "    yOUT_fit_2nd = np.zeros(len(ynew_fit))\n",
    "    for i in range(len(xnew_fit)):\n",
    "        #yOUT_fit[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "        #                               e2*np.cos(2*math.pi*f*xnew_fit[i])+\\\n",
    "        #                            e3*np.sin(4*math.pi*f*xnew_fit[i]) + \\\n",
    "        #                            e4*np.cos(4*math.pi*f*xnew_fit[i])+ \\\n",
    "        #                            (C+ (D*np.exp(-(xnew_fit[i]-eqT)/E) )  ) * (step(xnew_fit[i],eqT, sc)) #modified by an 20200421\n",
    "        yOUT_fit[i] = func_eq_step(xnew_fit[i], e1, e2, f, A, B, e3, e4, C, D, E, eqT, sc )\n",
    "\n",
    "        \n",
    "  #print yOUT[i]\n",
    "        resi_fit[i]=ynew_fit[i]-yOUT_fit[i]\n",
    "  #print resi[i]\n",
    "        yOUT_fit_1st[i]= A + B*xnew_fit[i]+e1*np.sin(2*math.pi*f*xnew_fit[i]) +\\\n",
    "                                    e2*np.cos(2*math.pi*f*xnew_fit[i])\n",
    "        yOUT_fit_2nd[i]= A + B*xnew_fit[i]+e3*np.sin(4*math.pi*f*xnew_fit[i]) +\\\n",
    "                                    e4*np.cos(4*math.pi*f*xnew_fit[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0636721805478\n",
      "1.96492733545\n",
      "96.7595656389\n"
     ]
    }
   ],
   "source": [
    "resi_sq= np.sum(resi_fit**2)\n",
    "print(resi_sq)\n",
    "data_sq= np.sum(ynew_fit**2)\n",
    "print(data_sq)\n",
    "VR = (1 - (resi_sq/data_sq)) * 100\n",
    "print(VR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e1 =  -0.00000 \n"
     ]
    }
   ],
   "source": [
    "s = \"e1 =  %.5f \"% (e1)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.651807971197\n",
      "0.967687457633\n"
     ]
    }
   ],
   "source": [
    "recovery_end_time = eqT + E\n",
    "print(recovery_end_time)\n",
    "recovery_end_time_sd = eqT + E +  math.sqrt(pcov[9, 9])\n",
    "print(recovery_end_time_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if recovery_end_time > 5:\n",
    "#    recovery_end_time = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute synthetic data\n",
    "max_year = 300\n",
    "time_synthetic = np.arange(0,max_year,0.01)\n",
    "dvv_synthetic = np.zeros(len(time_synthetic))\n",
    "for i in range(len(time_synthetic)):\n",
    "    dvv_synthetic[i] = func_eq_step(time_synthetic[i], e1, e2, f, A, B, e3, e4, C, D, E, eqT, sc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(dvv_synthetic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# dvv_static =  -0.255215005632 dvv_dynamic =  -0.000470714452634  D =  -0.255685720084\n"
     ]
    }
   ],
   "source": [
    "dvv_static = func_eq_step(recovery_end_time, e1, e2, f, A, B, e3, e4, C, D, E, eqT, sc)\n",
    "dvv_dynamic = D - dvv_static\n",
    "print(\"# dvv_static = \", dvv_static, \"dvv_dynamic = \", dvv_dynamic, \" D = \",D)\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "VROUTOPT = 1\n",
    "\n",
    "if VROUTOPT:\n",
    "    #vr_fi = \"VR.out_rmedian_center.\"+str(smooth_tw )+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".030.dttwithoffset.static_1.0_5.0_10.0_both_0.85_0.1_0.5.out\"\n",
    "    vr_fi = \"VR.out_\"+sta+\"_smtw\"+str(smooth_tw )+\"_\"+xc_para\n",
    "    if dvv_2sdOPT:\n",
    "        #dvv2sd_\"+str(dvv_2sd_max)+\".\"\n",
    "        #vr_fi = \"VR.out_rmedian_center.\"+str(smooth_tw )+\"_dvv2sd_\"+str(dvv_2sd_max)+\".\"+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".030.dttwithoffset.static_1.0_5.0_10.0_both_0.85_0.1_0.5.out\"\n",
    "        #vr_fi = \"VR.out_\"+sta+\".\"+xc_para+str(smooth_tw )+\"_dvv2sd_\"+str(dvv_2sd_max)\n",
    "        vr_fi = \"VR.out_\"+sta+\"_smtw\"+str(smooth_tw )+\"_dvv2sd_\"+str(dvv_2sd_max)+\"_\"+xc_para\n",
    "\n",
    "\n",
    "    fi = open(vr_fi, 'w')\n",
    "    #s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'\n",
    "    s = \"# VR = \"+str(VR)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# st_time = \"+str(st_time)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# et_time = \"+str(et_time)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# dttime = \"+str(dttime)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# smooth_tw  = \"+str( smooth_tw )+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "\n",
    "    \n",
    "    s = \"# e1 =  %.5f \"% (e1)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e2 =  %.5f \"% (e2)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e =  %.5f \"% (e)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "\n",
    "    s = \"# f  =  %.5f \"% (f)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# A =  %.5f \"% (A)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# B =  %.5f \"% (B)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# C =  %.5f \"% (C)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# D =  %.5f \"% (D)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# E =  %.5f \"% (E)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "\n",
    "    s = \"# eqT =  %.5f \"% (eqT)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "\n",
    "    s = \"# e1sd =  %.5f \"% (e1sd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e2sd =  %.5f \"% (e2sd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# fsd  =  %.5f \"% (fsd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# Asd =  %.5f \"% (Asd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# Bsd =  %.5f \"% (Bsd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "\n",
    "    s = \"# Csd =  %.5f \"% (Csd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# Dsd =  %.5f \"% (Dsd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# Esd =  %.5f \"% (Esd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# eqTsd =  %.5f \"% (eqTsd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    \n",
    "    if stepOPT == 1:\n",
    "    #if kOPT == 1:\n",
    "        s = \"# sc =  %.5f \"% (sc)+\"\\n\"\n",
    "        fi.write(s)\n",
    "\n",
    "        s = \"# scsd =  %.5f \"% (scsd)+\"\\n\"\n",
    "\n",
    "        fi.write(s)\n",
    "\n",
    "\n",
    "\n",
    "    s = \"# phi =  %.5f \"% (phi)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi_deg =  %.5f \"% (phi_deg)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi_deg2 =  %.5f \"% (phi_deg2)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e3 =  %.5f \"% (e3)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e4 =  %.5f \"% (e4)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e3sd =  %.5f \"% (e3sd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e4sd =  %.5f \"% (e4sd)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e4p =  %.5f \"% (e4p)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi4p =  %.5f \"% (phi4p)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi4p_deg =  %.5f \"% (phi4p_deg)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi4p_deg2 =  %.5f \"% (phi4p_deg2)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi_day =  %.5f \"% (phi_day)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# phi4p_day =  %.5f \"% (phi4p_day)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    s = \"# e_ratio =  %.5f \"% (e_ratio)+\"\\n\"\n",
    "    fi.write(s)\n",
    "\n",
    "    # dvv_static =  -0.355868939121 dvv_dynamic =  -0.195715874693  D =  -0.551584813815\n",
    "    s = \"# dvv_static =  %.5f \"% (dvv_static)+\"\\n\"\n",
    "    fi.write(s)\n",
    "    \n",
    "    s = \"# dvv_dynamic =  %.5f \"% (dvv_dynamic)+\"\\n\"\n",
    "    fi.write(s)   \n",
    "    \n",
    "    #recovery_end_time\n",
    "    s = \"# recovery_end_time =  %.5f \"% (recovery_end_time)+\"\\n\"\n",
    "    fi.write(s)   \n",
    "    \n",
    "    #f.write('2: 2nd line\\n')\n",
    "    #f.write('2: last line\\n')\n",
    "\n",
    "    fi.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VR = 96.75957\n",
      "e1 =  -0.00000 +/- 0.05862\n",
      "e2 = 0.00000 +/- 0.16927\n",
      "f =  1.00000 +/- 3168.88147\n",
      "A =  0.00085 +/- 0.15262\n",
      "B =  -0.00001 +/- 0.76122\n",
      "e3 =  0.00000 +/- 0.03298\n",
      "e4 = 0.00000 +/- 0.01880\n",
      "C =  -0.16301 +/- 0.78574\n",
      "D =  -0.25569 +/- 0.49631\n",
      "E =  0.08576 +/- 0.31588\n",
      "eqT = 0.56605 +/- 0.01259\n"
     ]
    }
   ],
   "source": [
    "print(\"VR = %.5f\" %VR)\n",
    "print(\"e1 =  %.5f +/- %.5f\" % (popt[0], math.sqrt(pcov[0, 0])))\n",
    "print(\"e2 = %.5f +/- %.5f\" % (popt[1], math.sqrt(pcov[1, 1])))\n",
    "print(\"f =  %.5f +/- %.5f\" % (popt[2], math.sqrt(pcov[2, 2])))\n",
    "print(\"A =  %.5f +/- %.5f\" % (popt[3], math.sqrt(pcov[3, 3])))\n",
    "print(\"B =  %.5f +/- %.5f\" % (popt[4], math.sqrt(pcov[4, 4])))\n",
    "print(\"e3 =  %.5f +/- %.5f\" % (popt[5], math.sqrt(pcov[5, 5])))\n",
    "print(\"e4 = %.5f +/- %.5f\" % (popt[6], math.sqrt(pcov[6, 6])))\n",
    "print(\"C =  %.5f +/- %.5f\" % (popt[7], math.sqrt(pcov[7, 7])))\n",
    "print(\"D =  %.5f +/- %.5f\" % (popt[8], math.sqrt(pcov[8, 8])))\n",
    "print(\"E =  %.5f +/- %.5f\" % (popt[9], math.sqrt(pcov[9, 9])))\n",
    "print(\"eqT = %.5f +/- %.5f\" % (popt[10], math.sqrt(pcov[10, 10])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "if h2OPT:\n",
    "    print(\"h1 = %.5f +/- %.5f\" % (popt[11], math.sqrt(pcov[11, 11]))) #modified by an 20200421\n",
    "    print(\"h2 = %.5f +/- %.5f\" % (popt[12], math.sqrt(pcov[12, 12]))) #modified by an 20200421"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sc = 32.26610 +/- 10.16979\n"
     ]
    }
   ],
   "source": [
    "if stepOPT:\n",
    "    print(\"sc = %.5f +/- %.5f\" % (popt[11], math.sqrt(pcov[11, 11]))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vr_fi = \"VR.out_\"+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".030.dttwithoffset.static_1.0_5.0_10.0_both_0.85_0.1_0.5.out\"\n",
    "#print(vr_fi)\n",
    "# store output in pandas dataframe\n",
    "#df = pd.DataFrame({ 'VR' : VRout\n",
    "#                  })\n",
    "# save \n",
    "#df.to_csv(vr_fi,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_timedf = pd.DataFrame(year_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "dvvOUTOPT = 0 \n",
    "\n",
    "if dvvOUTOPT:\n",
    "    dvvOUT_fi = \"dvvOUT_rmedian_center.\"+str(smooth_tw )+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".\"+paraIn+\".out\"\n",
    "\n",
    "    if dvv_2sdOPT:\n",
    "        #dvv2sd_\"+str(dvv_2sd_max)+\".\"\n",
    "        dvvOUT_fi = \"dvvOUT_rmedian_center.\"+str(smooth_tw )+\"_dvv2sd_\"+str(dvv_2sd_max)+\".\"+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".\"+paraIn+\".out\"\n",
    "\n",
    "\n",
    "    print(dvvOUT_fi)\n",
    "    df = pd.DataFrame({ 'ynew_fit' : ynew_fit,\n",
    "                        'xnew_fit': xnew_fit,\n",
    "                        'yOUT_fit' : yOUT_fit,\n",
    "                        'year_time': year_time})\n",
    "    df.to_csv(dvvOUT_fi,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitOUT_CI_DAW_CI_SRT_smtw1D_dvv2sd_0.2_ZRT.01.030.dttwithoffset.dynamic_2.0_20.0_30.0_both_0.85_0.1_0.5.out.png\n"
     ]
    }
   ],
   "source": [
    "pngOUTOPT = 1 \n",
    "\n",
    "if pngOUTOPT:\n",
    "    #pngOUT_fi = \"fitOUT_rmedian_center.\"+str(tw)+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".\"+paraIn+\".png\"\n",
    "    pngOUT_fi = \"fitOUT_\"+sta+\"_smtw\"+str(smooth_tw )+\"_\"+xc_para+\".png\"\n",
    "\n",
    "    if dvv_2sdOPT:\n",
    "        #dvv2sd_\"+str(dvv_2sd_max)+\".\"\n",
    "        #pngOUT_fi = \"fitOUT_rmedian_center.\"+str(tw)+\"_dvv2sd_\"+str(dvv_2sd_max)+\".\"+netIn+\"_\"+staIn+\"_\"+netIn+\"_\"+staIn+\".\"+xccomIn+\".\"+freqIn+\".\"+paraIn+\".png\"\n",
    "        pngOUT_fi = \"fitOUT_\"+sta+\"_smtw\"+str(smooth_tw )+\"_dvv2sd_\"+str(dvv_2sd_max)+\"_\"+xc_para+\".png\"\n",
    "\n",
    "        \n",
    "        \n",
    "print(pngOUT_fi)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(xnew_fit, yOUT_fit_2nd)\n",
    "#plt.plot(xnew_fit, yOUT_fit_1st)\n",
    "#plt.plot(xnew_fit,dvv_data['viso'],label='dvv' ) #modified by an 20200409\n",
    "\n",
    "xmin = 0\n",
    "xmax = 1\n",
    "#xmax = 300\n",
    "ymin = -0.5\n",
    "ymax = 0.5\n",
    "\n",
    "recovery_end_plot = pd.DataFrame({'time': [recovery_end_time, recovery_end_time],\n",
    "                    'val': [ymin, ymax]})\n",
    "\n",
    "\n",
    "dvv_static_plot = pd.DataFrame({'time': [decimal_time_lapse_dvv[0], decimal_time_lapse_dvv[-1]],\n",
    "                    'val': [dvv_static, dvv_static]})\n",
    "\n",
    "\n",
    "\n",
    "#plt.plot(decimal_time_lapse_dvv, dvv_median.dvv*1, 'o' , label='observed data after medinan filter with '+smooth_tw)\n",
    "\n",
    "plt.plot(xnew_fit, yOUT_fit,label='fitting')\n",
    "plt.plot(xnew_fit, ynew_fit,label='interpolated data with cubic spline')\n",
    "\n",
    "plt.plot(time_synthetic, dvv_synthetic,label='fitting')\n",
    "\n",
    "#plt.xlabel('Year from 2002') #modified by an 20200407\n",
    "plt.xlabel('Year from 2019') #modified by an 20200407\n",
    "#plt.ylabel('Viso (m/s)') #modified by an 20200407\n",
    "plt.ylabel('Dvv') #modified by an 20200407\n",
    "plt.title('Dvv of Pair: '+sta) #modified by an 20200409\n",
    "#plt.title('Viso Station: '+sta) #modified by an 20200409\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "#plt.ylim((210,240)) #modified by an 20200407\n",
    "plt.ylim((ymin,ymax)) #modified by an 20200407\n",
    "\n",
    "plt.xlim((xmin,xmax)) #modified by an 20200407\n",
    "\n",
    "# org\n",
    "#plt.savefig(\"C:/Users/an/STORAGE/RESEARCH/Tools/curve_fit/jupyter/output/\"+sta+'_curve_fit') #modified by an 20200424\n",
    "# new\n",
    "#plt.savefig(\"./\"+sta+'_curve_fit') #modified by an 20200424\n",
    "\n",
    "\n",
    "plt.plot(recovery_end_plot.time, recovery_end_plot.val, color='gray', linestyle='dashed',linewidth = 1.5)\n",
    "plt.plot(dvv_static_plot.time, dvv_static_plot.val, color='black', linestyle='dashed',linewidth = 1.5)\n",
    "\n",
    "\n",
    "if pngOUTOPT:\n",
    "    plt.savefig(pngOUT_fi)\n",
    "    \n",
    "    \n",
    "    "
   ]
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